Hello,
I am writing my research on the determinants of bribing. I am using interaction variables in my logistic regression. My data is from a survey with oversampling in six regions. After reading in this forum that I could use [control = pweight] in my command, I decided not to use svy: set, since I need to report my pseudo R2.
However, now I am not quite sure about the result, because I think the odds ratio of the interaction variable kis##health is too big. I am quite new with stata and statistic. Thus I need your advice on this matter.
Both my interaction variables are dummy: KIS = poor people = 1; health : poor perception on quality of health service = 1
my output is
Is there anything wrong with the data? Before I use the controls pweight the odds ratio is 7.
If there is nothing wrong: Is it right if I interpret it as: the odds ratio of poor people with poor perception on health service ten times more likely to bribe than not poor people with good perception on the health service. I find this sentence is wrong, but I don't know how to fix it.
Here is the dataex of my research.
Really appreciate your help on this matter
I am writing my research on the determinants of bribing. I am using interaction variables in my logistic regression. My data is from a survey with oversampling in six regions. After reading in this forum that I could use [control = pweight] in my command, I decided not to use svy: set, since I need to report my pseudo R2.
However, now I am not quite sure about the result, because I think the odds ratio of the interaction variable kis##health is too big. I am quite new with stata and statistic. Thus I need your advice on this matter.
Both my interaction variables are dummy: KIS = poor people = 1; health : poor perception on quality of health service = 1
my output is
Code:
logit brihealth kis##health urban age gender education employment business religius1 value $controls[pw = BOT_NAS_JBR_JTG], or robus > t nolog Logistic regression Number of obs = 1,373 Wald chi2(11) = 81.68 Prob > chi2 = 0.0000 Log pseudolikelihood = -210.62002 Pseudo R2 = 0.1182 Robust brihealth Odds Ratio Std. Err. z P>z [95% Conf. Interval] 1.kis .6619124 .179435 -1.52 0.128 .3890916 1.126028 1.health .6359187 .3877471 -0.74 0.458 .1924807 2.100951 kis#health 1 1 11.5063 8.774819 3.20 0.001 2.581076 51.29446 urban .6217023 .1611696 -1.83 0.067 .3740397 1.033349 age .9574096 .0090956 -4.58 0.000 .9397476 .9754036 gender .3653943 .1218295 -3.02 0.003 .190088 .7023746 education .8099218 .1092265 -1.56 0.118 .6217984 1.054961 employment .6651625 .2377409 -1.14 0.254 .3301363 1.340177 business 2.459535 .8240591 2.69 0.007 1.275442 4.742914 religius1 1.008044 .1638453 0.05 0.961 .7330385 1.386219 value .4081261 .0997453 -3.67 0.000 .2527913 .6589107 _cons 2.904753 2.118633 1.46 0.144 .695457 12.13244
If there is nothing wrong: Is it right if I interpret it as: the odds ratio of poor people with poor perception on health service ten times more likely to bribe than not poor people with good perception on the health service. I find this sentence is wrong, but I don't know how to fix it.
Here is the dataex of my research.
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(brihealth kis health urban age gender education employment business religius1 value) double BOT_NAS_JBR_JTG 0 1 0 0 54 0 0 1 0 4 1 .07809219214599998 . 0 0 0 22 1 2 1 0 4 1 .07809219214599998 . 1 0 0 64 0 1 1 0 4 0 .07809219214599998 . . 0 0 52 1 0 1 0 4 1 .07809219214599998 . 0 0 0 34 0 0 1 0 4 1 .07809219214599998 0 0 0 0 32 1 0 1 0 4 1 .07809219214599998 0 0 0 0 36 0 0 1 0 4 1 .07809219214599998 . 1 0 0 59 1 0 1 0 4 1 .07809219214599998 0 . 0 0 58 0 0 1 0 4 1 .07809219214599998 . . 0 0 28 1 2 1 0 4 1 .07809219214599998 0 0 0 0 19 0 2 0 0 . 1 .08627298325863374 . . . 0 57 1 0 0 0 . . .08627298325863374 0 . 0 0 50 0 0 1 0 4 1 .08627298325863374 0 . 0 0 45 1 0 0 0 3 1 .08627298325863374 0 0 0 0 37 0 1 1 0 4 1 .08627298325863374 . . 0 0 35 1 0 0 0 . . .08627298325863374 0 0 0 0 57 0 0 1 0 3 1 .08627298325863374 . . 0 0 50 1 0 0 0 . . .08627298325863374 0 0 0 0 53 0 0 1 0 3 1 .08627298325863374 . . 0 0 38 1 0 0 0 4 1 .08627298325863374 0 0 0 0 25 0 0 1 1 4 1 .09816057671983237 . . 0 0 24 1 2 0 0 4 1 .09816057671983237 . 0 0 0 67 0 0 0 0 4 1 .09816057671983237 0 . 0 0 42 1 0 1 0 3 1 .09816057671983237 0 0 0 0 45 0 0 1 0 4 1 .09816057671983237 1 0 0 0 30 1 0 0 0 4 1 .09816057671983237 . 1 0 0 55 0 2 1 0 4 1 .09816057671983237 0 0 0 0 27 1 2 0 0 4 1 .09816057671983237 0 1 0 0 44 0 1 1 0 4 1 .09816057671983237 0 0 0 0 46 1 0 1 0 4 1 .09816057671983237 0 0 0 0 36 0 1 1 0 3 1 .09816057671983237 0 0 0 0 28 1 2 0 0 4 1 .09816057671983237 0 0 0 0 29 0 1 1 0 3 1 .09816057671983237 0 0 0 0 27 1 2 0 0 3 1 .09816057671983237 0 . 0 0 59 0 2 1 0 4 1 .09816057671983237 0 0 1 0 23 1 2 0 0 4 1 .09816057671983237 . 0 0 0 58 0 2 1 1 4 1 .09816057671983237 0 1 0 0 42 1 1 1 1 3 1 .07417418612448407 . 0 0 0 60 0 0 1 0 4 1 .09816057671983237 0 1 1 0 39 1 0 0 0 3 1 .09816057671983237 . 1 0 0 48 0 1 1 0 4 1 .0868175767433184 . 0 0 0 70 1 0 1 0 4 1 .0868175767433184 . 1 0 0 47 0 0 1 0 4 1 .0868175767433184 . 1 0 0 30 1 0 1 0 4 1 .0868175767433184 . 1 0 0 35 0 1 1 0 4 1 .0868175767433184 . 1 0 0 35 1 2 0 0 4 1 .0868175767433184 . 0 0 0 45 0 2 1 0 4 1 .0868175767433184 0 1 0 0 40 1 0 1 0 4 1 .0868175767433184 0 0 0 0 48 0 2 1 0 3 1 .0868175767433184 0 0 0 0 36 1 1 0 0 3 0 .07529966521257946 0 1 0 0 31 0 2 1 0 4 1 .0868175767433184 . 0 0 0 52 1 0 1 0 4 0 .0868175767433184 . 0 0 0 23 0 2 1 0 3 1 .0868175767433184 . 1 0 0 22 1 1 0 0 3 1 .0868175767433184 . 0 0 0 60 0 0 1 0 4 1 .0868175767433184 . 0 0 0 31 1 0 1 0 3 1 .0868175767433184 . 0 0 0 34 0 1 1 0 4 1 .0868175767433184 . 0 0 0 39 1 0 1 0 4 1 .0868175767433184 0 0 0 0 67 0 0 0 0 4 1 .0868175767433184 0 0 0 0 41 1 3 1 0 4 1 .0868175767433184 0 0 0 0 40 0 1 1 0 4 1 .11662930745082345 0 . 1 0 41 1 0 0 0 4 1 .11662930745082345 0 . 1 0 50 0 0 1 0 4 1 .11662930745082345 0 1 0 0 35 1 0 0 0 3 1 .11662930745082345 0 0 0 0 19 0 1 1 0 4 1 .11662930745082345 0 0 0 0 46 1 1 1 0 4 1 .11662930745082345 0 0 0 0 21 0 2 1 0 4 1 .11662930745082345 0 . 1 0 26 1 1 1 0 4 1 .11662930745082345 0 1 0 0 50 0 0 1 0 4 1 .11662930745082345 0 1 1 0 25 1 3 0 0 3 1 .11662930745082345 1 . 0 0 35 0 2 0 0 4 1 .11662930745082345 0 0 0 0 40 1 2 0 0 4 1 .10115633417167323 . 0 0 0 48 0 0 1 0 4 1 .11662930745082345 . . 0 0 47 1 1 0 0 4 1 .11662930745082345 . 1 0 0 46 0 0 1 0 4 1 .11662930745082345 . . 0 0 19 1 3 0 0 4 1 .11662930745082345 . 0 0 0 46 0 0 1 0 4 1 .11662930745082345 . 0 0 0 42 1 2 1 0 4 1 .11662930745082345 . 0 0 0 29 0 3 1 0 4 1 .11662930745082345 0 1 0 0 23 1 3 0 0 4 1 .11662930745082345 . 0 0 1 52 0 2 1 0 4 1 .098587619521058 . 0 0 1 44 1 1 0 0 4 1 .098587619521058 . 0 0 1 37 0 1 1 0 4 1 .098587619521058 0 1 0 1 35 1 0 0 0 4 1 .098587619521058 . . 0 1 49 0 0 1 0 4 1 .098587619521058 . 0 0 1 32 1 2 0 0 4 1 .098587619521058 . 1 0 1 49 0 0 1 0 4 1 .098587619521058 . . 0 1 19 1 1 0 0 4 1 .098587619521058 0 1 0 1 19 0 2 0 0 4 1 .098587619521058 . 1 0 1 50 1 0 0 0 4 1 .098587619521058 . 1 0 1 46 0 2 1 0 4 0 .07209680654501288 . 1 0 1 41 1 2 1 0 3 0 .07209680654501288 . 0 0 1 46 0 2 1 0 4 1 .07209680654501288 . 1 0 1 39 1 1 0 0 4 1 .07209680654501288 . . 1 1 60 0 3 1 0 3 1 .07209680654501288 . 0 0 1 34 1 0 1 1 3 1 .07209680654501288 . 1 0 1 57 0 2 1 1 3 1 .05447932486087617 . 0 0 1 51 1 3 1 0 3 1 .07209680654501288 . 0 0 1 42 0 1 1 0 3 1 .07209680654501288 . 0 0 1 46 1 3 1 0 4 1 .06253186969023976 end
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